In daily and monthly time scale, we used this approach a lot Rs_mat_Rs_diurnal ()
We have much more measurements Rs in mid-latitude regions, where developed countries are mostly located
It is difficult to measure soil respiration all year around in cold regions, but critical because of high rates of climate change and large soil C stocks
Less-developed countries are constrained by lack of resources, and thus we do not have enough measurements from tropical regions (Xu and Shang 2016)
Global spatial distribution of soil respiration sites
Rs data from cold regions are more important, but how to increase the number of measurements? * Make equipment work normally in very cold and remote conditions * Increase resources devoted to Rs measurements * Methodological improvements: for instance, measuring once per day to get daily mean
Rs measured at diurnal soil temperature
Rs measured at mean annual soil temperature
Rs_annual ~ Rs_mat (“Rs_bahn”)Rs_annual = 455.8 * Rs_mat^1.0054 (R2 = 0.94, p<0.001)Data
Rs_Annual## Warning: Removed 147 rows containing non-finite values (stat_smooth).
## Warning: Removed 147 rows containing missing values (geom_point).
## Warning: Removed 687 rows containing non-finite values (stat_smooth).
## Warning: Removed 687 rows containing missing values (geom_point).
## Warning: Removed 125 rows containing non-finite values (stat_smooth).
## Warning: Removed 125 rows containing missing values (geom_point).
## Warning: Removed 696 rows containing non-finite values (stat_smooth).
## Warning: Removed 696 rows containing missing values (geom_point).
Statistics
Rs_mat based on the annual mean soil temperature, T_Annual, and/or MATRs_annual based on Rs_matRs_annual and Rs_annual_bahn to evaluate the performance of Bahn model across the globalUpdate Bahn’s model
## Wed Apr 3 17:58:33 2019 -------------------+++++-------------------
## Wed Apr 3 17:58:33 2019 Bahn relationship for these data:
##
## Call:
## lm(formula = Rs_TAIR ~ Rs_annual, data = sdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1006.45 -84.72 14.90 93.38 964.58
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.64266 13.67584 0.047 0.963
## Rs_annual 0.85184 0.01466 58.126 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 185.1 on 821 degrees of freedom
## Multiple R-squared: 0.8045, Adjusted R-squared: 0.8043
## F-statistic: 3379 on 1 and 821 DF, p-value: < 2.2e-16
## [1] "test intercept=0 and slope=1"
## [1] "p_intercept = 0.9625, p_slope = 0"
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